Malicious Nodes Detection in MANET Using Back-Off Clustering Approach

Mobile Ad hoc Networks (MANET) are infrastructure less networks which provide multi-hop wireless links between nodes. The main applications of MANET in real time environment are military and emergency areas where the fixed infrastructure is not required. It is a temporary communication infrastructure network for quick communication with minimal configuration settings among the group of nodes. The security is one of the primary concerns in MANET. The malicious nodes in MANET environment degrade the performance of the network. In this paper, the nodes in MANET are grouped using back-off duration technique and further the malicious nodes are detected using this algorithm. The proposed clustering based malicious nodes detection in MANET achieves higher performance in terms of packet delivery ratio, latency and energy consumption. The proposed method achieves 89.35% of packet delivery ratio, 36.2 ms latency and 26.91 mJ of energy consumption.

Behavior analysis of malicious sensor nodes based on optimal response dynamics

Wireless sensor networks are extremely vulnerable to various security threats.The intrusion detection method based on game theory can effectively balance the detection rate and energy consumption of the system.The accurate analysis of the attack behavior of malicious sensor nodes can help to configure intrusion detection system,reduce unnecessary system consumption and improve detection efficiency.However,the completely rational assumption of the traditional game model will cause the established model to be inconsistent with the actual attack and defense scenario.In order to formulate a reasonable and effective intrusion detection strategy,we introduce evolutionary game theory to establish an attack evolution game model based on optimal response dynamics,and then analyze the attack behavior of malicious sensor nodes.Theoretical analysis and simulation results show that the evolution trend of attacks is closely related to the number of malicious sensors in the network and the initial state of the strategy,and the attacker can set the initial strategy so that all malicious sensor nodes will eventually launch attacks.Our work is of great significance to guide the development of defense strategies for intrusion detection systems.

Avoid Suspicious Route of Blackhole Nodes in MANET’s:Using A Cooperative Trapping

Mobile Ad hoc Network(MANET)is decentralized wireless network and can communicate without existing infrastructure in many areas.MANET is vulnerable to various attacks that affect its performance such as blackhole attack.Blackhole attacker,inject fault routing information to persuade the source node to select the path with malicious node as the shortest path.To eliminate malicious nodes from launching any collaborative attack.A cooperative Trapping Approach(CTA)was proposed based on modifying Ad-hoc On-demand Distance Vector(AODV)routing protocol and trapping the malicious nodes by responding to the trap request message.The approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any attack.The approach realizes a backward tracking mechanism to perform the elimination process.The proposed algorithm(CTA)was executed using NS-2 network simulator.The performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput,end to end delay,packet delivery ratio,and consuming energy.The experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

Performance Analysis of Malicious Node Detection and Elimination Using Clustering Approach on MANET

Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of thousands of nodes that are mobile as well as autonomous and they do not requires any existing network infrastructure. The autonomous nodes can freely and randomly move within the network which can create temporary dynamic network and these networks can change their topology frequently. The security is the primary issue in MANET which degrades the network performance significantly. In this paper, cluster based malicious node detection methodology is proposed to detect and remove the malicious nodes. Each node within the cluster gets the cluster key from the cluster head and this key is used for the data transaction between cluster head and node. The cluster head checks this key for every data transaction from node and match with their cluster table. If match is valid, and then only it will recognize that this node is belongs to this cluster, otherwise it is decided as malicious node. This paper also discusses the detection of link failure due to the presence of malicious node by determining the gain of each link in the network. The performance of the proposed method is analyzed using packet delivery ratio, network life time, and throughput and energy consumption. The proposed malicious node detection system is compared with the conventional techniques as OEERP (Optimized energy efficient routing protocol), LEACH (Low energy adaptive clustering hierarchy), DRINA (Data routing for In-network aggregation) and BCDCP (Base station controlled dynamic clustering protocol).

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detectionfor transmitting and creating new messages or existing message modifications.The attacker on another node evaluates the forging activity in themessage directly or indirectly. Every node sends short packets in a MANETenvironment with its identifier, location on the map, and time through beacons.The attackers on the network broadcast the warning message usingfaked coordinates, providing the appearance of a network collision. Similarly,MANET degrades the channel utilization performance. Performancehighly affects network performance through security algorithms. This paperdeveloped a trust management technique called Enhanced Beacon TrustManagement with Hybrid Optimization (EBTM-Hyopt) for efficient clusterhead selection and malicious node detection. It tries to build trust amongconnected nodes and may improve security by requiring every participatingnode to develop and distribute genuine, accurate, and trustworthy materialacross the network. Specifically, optimized cluster head election is done periodicallyto reduce and balance the energy consumption to improve the lifetimenetwork. The cluster head election optimization is based on hybridizingParticle Swarm Optimization (PSO) and Gravitational Search OptimizationAlgorithm (GSOA) concepts to enable and ensure reliable routing. Simulationresults show that the proposed EBTM-HYOPT outperforms the state-of-thearttrust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13%of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay,and 16.34% of packet length.

A Dynamic Bayesian-Based Comprehensive Trust Evaluation Model for Dispersed Computing Environment

Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.

Securing Sensor Networks Based on Optimization of Weighted Confidence

In this paper, an optimized rmlicious nodes detection algorithm, based on Weighted Confidence Filter （WCF）, is proposed to protect sensor networks from attacks. In this algorithm, each cluster head in a cluster-based hierarchical network figures out an average confidence degree by means of messages from its child nodes. The cluster head only accepts a message from the child node whose confidence degree is higher than the average. Meanwhile, it updates the confidence degrees for each of its child nodes by comparing the aggregation value and the received messages, and regards them as the weight of exactness of messages from nodes. A sensor node is judged to be rmlicious if its weight value is lower than the predefined threshold. Comparative simulation results verify that the proposed WCF algorithm is better than the Weighted Trust Evaluation （WTE） in terms of the detection ratio and the false alarm ratio. More specifically, with the WCF, the detection ratio is significantly improved and the false alarm ratio is observably reduced, especially when the malicious node ratio is 0.25 or greater. When 40% of 100 sensors are malicious, the detection accuracy is above 90% and the false alarm ratio is nearly only 1.8%.

SAC-TA: A Secure Area Based Clustering for Data Aggregation Using Traffic Analysis in WSN

Clustering is the most significant task characterized in Wireless Sensor Networks (WSN) by data aggregation through each Cluster Head (CH). This leads to the reduction in the traffic cost. Due to the deployment of the WSN in the remote and hostile environments for the transmission of the sensitive information, the sensor nodes are more prone to the false data injection attacks. To overcome these existing issues and enhance the network security, this paper proposes a Secure Area based Clustering approach for data aggregation using Traffic Analysis (SAC-TA) in WSN. Here, the sensor network is clustered into small clusters, such that each cluster has a CH to manage and gather the information from the normal sensor nodes. The CH is selected based on the predefined time slot, cluster center, and highest residual energy. The gathered data are validated based on the traffic analysis and One-time Key Generation procedures to identify the malicious nodes on the route. It helps to provide a secure data gathering process with improved energy efficiency. The performance of the proposed approach is compared with the existing Secure Data Aggregation Technique (SDAT). The proposed SAC-TA yields lower average energy consumption rate, lower end-to-end delay, higher average residual energy, higher data aggregation accuracy and false data detection rate than the existing technique.

TMTACS: Two-Tier Multi-Trust-Based Algorithm to Countermeasure the Sybil Attacks

Mobile Ad hoc Networks(MANETs)have always been vulnerable to Sybil attacks in which users create fake nodes to trick the system into thinking they’re authentic.These fake nodes need to be detected and deactivated for security reasons,to avoid harming the data collected by various applications.The MANET is an emerging field that promotes trust management among devices.Transparency is becoming more essential in the communication process,which is why clear and honest communication strategies are needed.Trust Management allows for MANET devices with different security protocols to connect.If a device finds difficulty in sending a message to the destination,the purpose of the communication process won’t be achieved and this would disappoint both that device and all of your devices in general.This paper presents,the Two-Tier Multi-Trust based Algorithm for Preventing Sybil Attacks in MANETs(TMTACS).The TMTACS provides a two-tier security mechanism that can grant or revoke trust in the Nodes of the MANET.It’s a smart way to identify Sybil nodes in the system.A proficient cluster head selection algorithm is also defined,which selects cluster head efficiently and does load balancing to avoid resource consumption from a single node only.Also,for routing efficient path is selected to deteriorate energy consumption and maximize throughput.The recent technique is compared with Secured QoS aware Energy Efficient Routing(SQEER),Adaptive Trust-Based Routing Protocol(ATRP),and Secure Trust-Aware Energy-Efficient Adaptive Routing(STEAR)in terms of Packet Delivery Ratio(PDR),consumption of energy etc.The simulation was performed on MATrix LABoratory(MATLAB)and the results achieved by the present scheme are better than existing techniques.

Adversarial attacks against dynamic graph neural networks via node injection

Dynamic graph neural networks(DGNNs)have demonstrated their extraordinary value in many practical applications.Nevertheless,the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance.At the same time,current adversarial attack schemes are implemented on static graphs,and the variability of attack models prevents these schemes from transferring to dynamic graphs.In this paper,we use the diffused attack of node injection to attack the DGNNs,and first propose the node injection attack based on structural fragility against DGNNs,named Structural Fragility-based Dynamic Graph Node Injection Attack(SFIA).SFIA firstly determines the target time based on the period weight.Then,it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject.Finally,an optimization function is designed to generate adversarial features for malicious nodes.Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches.When the graph is injected with 1%of the original total number of nodes through SFIA,the link prediction Recall and MRR of the target DGNN link decrease by 17.4%and 14.3%respectively,and the accuracy of node classification decreases by 8.7%.

Privacy preserving secure expansive aggregation with malicious node identification in linear wireless sensor networks

The Wireless Sensor Networks(WSNs)used for the monitoring applications like pipelines carrying oil,water,and gas;perimeter surveillance;border monitoring;and subway tunnel monitoring form linearWSNs.Here,the infrastructure being monitored inherently forms linearity(straight line through the placement of sensor nodes).Therefore,suchWSNs are called linear WSNs.These applications are security critical because the data being communicated can be used for malicious purposes.The contemporary research of WSNs data security cannot fit in directly to linear WSN as only by capturing few nodes,the adversary can disrupt the entire service of linear WSN.Therefore,we propose a data aggregation scheme that takes care of privacy,confidentiality,and integrity of data.In addition,the scheme is resilient against node capture attack and collusion attacks.There are several schemes detecting the malicious nodes.However,the proposed scheme also provides an identification of malicious nodes with lesser key storage requirements.Moreover,we provide an analysis of communication cost regarding the number of messages being communicated.To the best of our knowledge,the proposed data aggregation scheme is the first lightweight scheme that achieves privacy and verification of data,resistance against node capture and collusion attacks,and malicious node identification in linear WSNs.